
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "54"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = os.environ["ANTENNA"]
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "67"
csv_folder = "/home/obs/src/H5C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H5C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, '*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 79 csvs in /home/obs/src/H5C_Notebooks/_rtp_summary_
Found 75 auto_metrics notebooks in /home/obs/src/H5C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        antnum = row['Ant']
        if antnum != int(antenna):
            continue
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


html_to_display = ''
for am_html in auto_metric_htmls:
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line: 
            break


HTML(html_to_display)

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459594	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	8.777971	-0.077480	78.409046	11.379098	21.757134	4.275299	29.184809	12.736677	0.0342	0.0594	0.0429	1.174449	1.214594
2459593	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	15.797713	1.176523	82.223611	14.862369	31.354130	5.832803	13.540435	3.156235	0.0347	0.0695	0.0488	1.169398	1.207220
2459592	digital_ok	100.00%	100.00%	100.00%	0.00%	-	-	1.718443	-0.493731	7.275397	2.279672	0.664738	5.249139	-1.606545	0.260126	0.0276	0.0251	0.0012	nan	nan
2459591	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	18.227605	2.057148	96.169968	17.658956	26.653106	4.702689	1.480084	1.019020	0.0348	0.0767	0.0491	1.202954	1.243666
2459590	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	20.857844	2.147221	89.235166	15.445631	28.787768	4.335436	2.905661	0.620876	0.0364	0.0786	0.0435	1.178259	1.222229
2459589	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	20.052815	2.131377	83.001375	13.715767	16.677967	4.236100	19.035479	7.057195	0.0345	0.0603	0.0441	1.172680	1.207813
2459588	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	14.121057	1.050260	100.922850	17.412439	21.865312	3.711152	14.460781	4.462533	0.0320	0.0563	0.0416	1.186804	1.230586
2459587	digital_ok	100.00%	-	-	-	100.00%	0.00%	18.628519	2.126601	134.806570	25.278367	6.329897	0.757971	10.380113	-0.088462	nan	nan	nan	1.089433	54.563371
2459586	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	16.317240	4.121759	31.634750	8.598687	9.895921	9.126001	5.124949	2.112239	0.0334	0.0589	0.0462	1.230610	1.266758
2459585	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	26.242140	15.829246	10.213258	8.073514	9.351957	10.437793	1.396776	2.343515	0.0295	0.0267	0.0016	0.000000	0.000000
2459584	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	22.661263	2.585424	111.505057	17.994108	15.539611	-0.862945	9.493774	0.106601	0.0312	0.0644	0.0400	1.258171	38.316915
2459583	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	16.528696	1.373167	34.369399	4.827611	19.859830	1.710095	0.027272	0.343011	0.0342	0.0695	0.0474	0.000000	0.000000
2459582	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	14.421559	1.431256	30.918823	4.369429	23.904271	2.304740	0.047714	0.244812	0.0305	0.0568	0.0357	1.166358	1.198787
2459581	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	20.077684	2.482261	35.829403	4.341783	18.798090	1.751695	0.269207	0.210144	0.0319	0.0605	0.0438	1.189166	1.231046
2459580	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	15.306350	1.694425	35.974174	4.674411	20.911001	1.744034	-0.186199	-0.087636	0.0314	0.0591	0.0351	0.000000	0.000000
2459579	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459578	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	2.271825	-0.997174	2.824686	-0.198267	0.189237	-0.311007	0.789885	0.603854	0.0277	0.0260	0.0009	nan	nan
2459577	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	19.615532	1.847600	33.254920	3.961194	20.946162	1.662915	0.103579	0.146342	0.0309	0.0613	0.0266	1.204512	1.240831
2459576	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459575	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459574	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	14.154357	1.314567	25.124674	2.989992	20.622079	1.984048	1.144844	0.492311	0.0320	0.0609	0.0463	1.234075	1.266587
2459573	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	14.646397	1.527753	43.248380	5.653410	24.705437	1.910160	0.707601	0.851662	0.0333	0.0662	0.0447	0.000000	0.000000
2459572	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	14.604279	1.229206	25.245377	3.043921	15.942935	1.618186	1.897145	1.562938	0.0326	0.0715	0.0461	1.199054	1.250421
2459571	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	1.766144	-0.821565	2.727974	-0.280423	0.552671	-0.912677	0.603144	0.908561	0.0286	0.0254	0.0019	nan	nan
2459570	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	19.262351	4.557198	26.926052	2.790592	1.341841	2.965029	3.314352	2.351808	0.0437	0.0480	0.0481	1.207484	1.230274
2459569	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	19.246177	1.746885	33.188187	3.568664	10.406308	3.820558	8.153148	4.178102	0.0352	0.0693	0.0468	1.200514	1.246967
2459566	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	14.808677	1.524979	33.002084	4.285424	18.878293	1.961832	-0.371242	0.086603	0.0311	0.0633	0.0420	1.187625	1.219728
2459565	digital_ok	0.00%	-	-	-	100.00%	0.00%	-0.160317	-0.160317	-0.179841	-0.179841	0.501946	0.501946	0.023484	0.023484	nan	nan	nan	0.000000	0.000000
2459564	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	13.029775	1.426083	34.950952	4.589712	2.364077	0.929809	0.296101	0.727492	0.0329	0.0744	0.0463	1.178247	1.217678
2459563	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	15.159658	1.613114	66.898661	9.008385	19.531655	2.170233	1.125561	0.599584	0.0348	0.0702	0.0504	1.262674	1.306667
2459562	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	9.405436	1.263255	17.182633	2.516216	2.418881	1.842370	0.493721	0.824723	0.0326	0.0714	0.0491	1.188758	1.226494
2459561	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	10.253267	1.289853	16.379614	2.283332	2.798196	1.422834	0.788619	1.079293	0.0326	0.0661	0.0399	1.215285	1.253705
2459560	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	9.287548	0.984971	16.813098	2.249386	2.546247	1.401602	0.501565	0.914738	0.0320	0.0669	0.0517	1.186076	1.218752
2459559	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459558	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	16.361153	1.468980	34.462315	4.606909	21.527693	2.417148	1.018516	0.835595	0.0334	0.0743	0.0483	1.207679	1.254331
2459557	digital_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0299	0.0252	0.0010	nan	nan
2459556	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	16.183504	1.723308	37.200883	4.801641	19.470595	1.798192	2.111817	1.777530	0.0322	0.0713	0.0395	1.204202	1.249699
2459554	digital_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0319	0.0695	0.0476	nan	nan
2459553	digital_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0304	0.0571	0.0363	nan	nan
2459552	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	18.887017	2.087386	50.183875	5.399457	29.113071	1.977021	2.908488	1.248775	0.0322	0.0596	0.0463	0.000000	0.000000
2459551	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	9.239840	0.895848	20.323074	2.625976	3.344529	1.825199	0.848868	1.541323	0.0324	0.0741	0.0489	1.200996	1.241308
2459550	digital_ok	-	98.35%	98.35%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0349	0.0320	0.0010	nan	nan
2459549	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	14.237296	1.223557	37.749248	4.694548	20.880630	1.890637	0.776673	0.551817	0.0325	0.0685	0.0484	1.215888	1.253063
2459542	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	20.221830	1.963891	76.604212	9.717489	1.087040	-0.475033	0.752081	0.572919	0.0335	0.0748	0.0467	1.206285	1.246677
2459541	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	10.641562	1.832759	38.041320	5.132674	218.205688	5.402673	6.695894	3.475866	0.0335	0.0710	0.0518	1.119598	1.155663
2459540	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	13.418173	1.176372	17.089316	2.427606	4.120716	1.104039	0.569901	0.992788	0.0326	0.0699	0.0496	1.239890	1.274002
2459536	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	24.131988	3.226291	62.442161	4.256764	29.457389	1.678629	1.574116	2.351928	0.0294	0.0637	0.0442	0.833789	0.863649
2459535	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	10.050629	0.568920	15.180001	1.368206	2.258498	0.595568	-0.059824	0.422717	0.0337	0.0811	0.0463	1.222566	1.275419
2459534	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	10.749345	0.373158	14.101998	1.269920	2.300719	0.147060	-0.132463	0.528387	0.0311	0.0696	0.0440	1.174287	1.214002
2459533	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	17.115324	1.723480	35.183562	4.393942	18.003455	1.492126	0.811369	1.657607	0.0314	0.0685	0.0466	1.192139	1.229671
2459532	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	19.659542	1.927923	47.396743	6.273145	22.313021	0.944592	0.434376	0.985656	0.0323	0.0672	0.0445	0.824529	0.864382
2459530	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	9.145032	0.763587	14.330273	1.210135	3.465322	1.415083	0.264826	0.777246	0.0321	0.0692	0.0491	1.179631	1.225404
2459527	digital_ok	0.00%	-	-	-	100.00%	0.00%	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459524	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	23.828617	2.092013	53.296451	5.193963	40.453626	10.362373	-0.104162	0.376552	0.0305	0.0631	0.0472	0.000000	0.000000
2459523	digital_ok	100.00%	-	-	-	100.00%	0.00%	21.147410	2.681963	49.520148	3.795907	39.592397	27.872423	13.741775	15.539041	nan	nan	nan	1.410883	1.445917
2459522	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	16.445521	2.349234	48.773286	3.284656	10.767557	4.335572	0.640784	1.552829	0.0311	0.0665	0.0528	0.843079	0.885692
2459513	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	2.295320	-0.780018	1.665309	-1.023865	-0.048146	0.892476	-0.122328	1.512269	0.0308	0.0274	0.0018	nan	nan
2459508	digital_ok	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	24.355425	2.562256	49.832995	4.205407	18.103721	5.571665	12.848301	7.810017	0.0425	0.9597	0.4235	0.000000	0.000000
2459505	digital_ok	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	20.508637	1.806856	44.677865	3.300506	6.972623	2.496937	0.239662	3.055954	0.0370	0.8641	0.5578	1.207228	18.356816
2459503	digital_ok	100.00%	100.00%	0.00%	0.00%	100.00%	0.00%	24.177553	4.605163	82.361955	4.815692	33.774532	12.320577	1.954648	1.819877	0.0320	0.8713	0.6129	0.000000	0.000000

Antenna Season Report Notebook¶

Antenna 67 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 67, Node 3:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 67: 2459602

Antenna 67: 2459598

Antenna 67: 2459597

Antenna 67: 2459596

Antenna 67: 2459595

Antenna 67: 2459594

Antenna 67: 2459593

Antenna 67: 2459592

Antenna 67: 2459591

Antenna 67: 2459590

Antenna 67: 2459589

Antenna 67: 2459588

Antenna 67: 2459587

Antenna 67: 2459586

Antenna 67: 2459585

Antenna 67: 2459584

Antenna 67: 2459583

Antenna 67: 2459582

Antenna 67: 2459581

Antenna 67: 2459580

Antenna 67: 2459579

Antenna 67: 2459578

Antenna 67: 2459577

Antenna 67: 2459576

Antenna 67: 2459575

Antenna 67: 2459574

Antenna 67: 2459573

Antenna 67: 2459572

Antenna 67: 2459571

Antenna 67: 2459570

Antenna 67: 2459569

Antenna 67: 2459566

Antenna 67: 2459565

Antenna 67: 2459564

Antenna 67: 2459563

Antenna 67: 2459562

Antenna 67: 2459561

Antenna 67: 2459560

Antenna 67: 2459559

Antenna 67: 2459558

Antenna 67: 2459556

Antenna 67: 2459552

Antenna 67: 2459551

Antenna 67: 2459549

Antenna 67: 2459542

Antenna 67: 2459541

Antenna 67: 2459540

Antenna 67: 2459536

Antenna 67: 2459535

Antenna 67: 2459534

Antenna 67: 2459533

Antenna 67: 2459532

Antenna 67: 2459530

Antenna 67: 2459527

Antenna 67: 2459524

Antenna 67: 2459523

Antenna 67: 2459522

Antenna 67: 2459513

Antenna 67: 2459508

Antenna 67: 2459506

Antenna 67: 2459505

Antenna 67: 2459503

Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶